Best practices in statistical computing

Abstract

The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well.

Keywords: data management; methodology; version control.

FIGURE 1

Key strategies for code quality assurance (QA) [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Sample strategies for writing clean code using case study data. Code snippet can be found here: https://github.com/jpane24/code-qa/blob/main/code/R00-Data-Cleaning.R [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Example README file for the code-qa github. README example from: https://github.com/jpane24/code-qa [Colour figure can be viewed at wileyonlinelibrary.com]

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Example of commented code for a function. Code snippet can be found here: https://github.com/jpane24/code-qa/blob/main/code/helper.R [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 5

Example of documenting a key analytic decision for handling missing data. Code snippet can be found here: https://github.com/jpane24/code-qa/blob/main/code/R00-Data-Cleaning.R [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 6

Example of commits and merge [Colour figure can be viewed at wileyonlinelibrary.com]